Article Per Year (5 Year)
p-Index From 2020 - 2025
1.556
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Journal of Technopreneurship and Information System (JTIS) Indonesian Journal of Intellectual Publication (IJI Publication) Jurnal Mandiri IT Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Bangsa JIM ( Journal International Multidiciplinary)
Luluk Elvitaria, Luluk
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search

 1 
Sistem Monitoring Kondisi Asset Berbasis Android salamun, salamun; Gianto, gianto; Elvitaria, Luluk
Journal of Technopreneurship and Information System (JTIS) Vol 2, No 3 (2019): Journal of Technopreneurship and Information System (JTIS)
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jtis.v2i3.513

Abstract

As a means of supporting the lecture process, the Abdurrrab University Informatics Engineering Study Program has a computer laboratory. But in monitoring its assets are still carried out conventionally. In the era of technology as it is today, this method is felt to be less effective. In addition to the problem of physical archives that require space and risk of losing files, data management is an important issue in this regard. To overcome this problem, we need an Android-based Laboratory Asset Monitoring System which data management is centralized to the database. In this Laboratory Asset Monitoring System, the admin can input data in the form of asset data and user data. The data inputted by the admin is then stored in a database and processed by the system into asset information such as asset life information and asset depreciation and user information. To calculate depreciation of assets, this system uses the Straight Line Depreciation Method. While laboratory personnel can check assets regularly to find out the current condition and status of assets. Asset checking data will also be saved to the database and can be called whenever needed.
Edukasi Internet Of Things Untuk Instansi Pendidikan Berbasis Ramah Lingkungan Di SMK Abdurrab Pekanbaru Arisandi, Diki; Trisnawati, Liza; Elvitaria, Luluk; Hartati, Seri; Ningrum, Puspa; Saputra, Haris Tri
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 11 (2024): Januari
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i11.642

Abstract

SMK Abdurrab Pekanbaru telah dipilih sebagai pilot project berbasis Internet of Things (IoT), mendapatkan kepercayaan dari Kementerian Pendidikan dan Kebudayaan. Namun, tantangan muncul karena para guru belum memiliki pemahaman dan keterampilan terkait penerapan IoT, khususnya dalam konteks penghematan energi untuk menciptakan sekolah yang ramah lingkungan. Kegiatan pengabdian ini bertujuan untuk memberikan pemahaman kepada para guru di SMK Abdurrab mengenai konsep dan implementasi IoT untuk penghematan energi dan keberlanjutan lingkungan. Dengan demikian, diharapkan dapat menjawab kepercayaan yang diberikan oleh Kementerian Pendidikan dan Kebudayaan terhadap pilot project SMK berbasis Revolusi Industri 4.0. Metode yang digunakan melibatkan survei awal dan wawancara di SMK Abdurrab Pekanbaru untuk mengevaluasi kebutuhan dan pemahaman awal. Selanjutnya, dilaksanakan pelatihan intensif IoT bagi para guru sebagai langkah nyata implementasi. Hasil dari kegiatan ini adalah terselenggaranya pelatihan IoT yang sukses, meningkatkan pemahaman dan keterampilan guru terkait penghematan energi dan keberlanjutan lingkungan di lingkungan sekolah. Dampak yang dirasakan oleh mitra, yaitu SMK Abdurrab, adalah penguatan pengetahuan dan keterampilan mereka dalam mengimplementasikan teknologi IoT untuk penghematan energi, menjadikan sekolah sebagai lembaga yang berkontribusi pada lingkungan yang lebih berkelanjutan.
Disaster Prevention Management Governance Model Forest and Land Fires Based on Ecotourism in Riau Syamsuadi, Amir; Yahya, MHD Rafi; Anugerah, M Fajar; Farras, Aqil; Trisnawati, Liza; Elvitaria, Luluk
JIM - Journal International Multidisciplinary Vol. 2 No. 1 (2024)
Publisher : Rumah Jurnal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jim.v2i1.985

Abstract

Riau Province is one of the regions in Indonesia that frequently experiences forest and land fires, which cause economic losses, environmental damage and threats to public health. This research aims to build a model for preventing forest and land fire disasters through an ecotourism approach, with a focus on integrating conservation efforts and empowering local communities. The research method used is a qualitative approach with case studies in several ecotourism areas in Riau. Data was collected through in-depth interviews with stakeholders, field observations, and analysis of related documents. The research results show that ecotourism has significant potential in preventing forest and land fires, especially through increasing environmental awareness among local communities and visitors, as well as developing environmentally friendly land management practices. The proposed prevention model includes several key elements: (1) active community participation in managing ecotourism areas; (2) sustainable environmental training and education; (3) collaboration between government, private sector and society in monitoring and enforcing the law; and (4) development of ecotourism infrastructure that supports nature conservation. Implementation of this model is expected to significantly reduce the risk of forest and land fires in Riau, while encouraging economic growth through sustainable ecotourism.
Kebijakan Mitigasi Kebakaran Hutan Dan Lahan Berbasis Pemberdayaan Desa Wisata Digital Sadar Bencana (DWDSB) Di Riau Syamsuadi, Amir; Arisandi, Diki; Hartati, Seri; Trisnawati, Liza; Elvitaria, Luluk; Nugroho, Sapto Setyo
Innovative: Journal Of Social Science Research Vol. 3 No. 6 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Riau Province is confirmed as the largest and most affected area by forest fires from 2015. This study aims to look at forest fire disaster mitigation based on social engineering of tourism villages based on economic empowerment in Riau Province. The method used in this research is mix method, which is a combination of quantitative and qualitative approaches. This research explains the Disaster Mitigation Policy Model for Forest and Land Fires in Disaster Aware Digital Tourism Village (DWDSB) Based on Economic Empowerment in Riau by describing the mitigation policy for forest and land fires (karhutla), the level of village readiness in facing forest and land fires and the analysis of disaster-aware digital tourism villages (DWDSB) on local economic empowerment in Riau. The results showed that Riau Governor Regulation Number 9 of 2020 became the legal basis for the Riau Province Regional Disaster Management Agency (BPBD) in the aspect of forest and land fire disaster mitigation. The results also show that the Disaster Aware Digital Tourism Village (DWDSB) can be an alternative to developing disaster mitigation-based social engineering policies through green economy-based community empowerment activities based on local wisdom.
Analisis Pengembangan Pariwisata Halal di Kecamatan Siak Syamsuadi, Amir; Trisnawati , Liza; Elvitaria, Luluk
Indonesian Journal of Intellectual Publication Vol. 1 No. 3 (2021): Juli, 2021, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v1i3.131

Abstract

Penelitian ini mengidentifikasi pengembangan destinasi wisata di Kecamatan Siak dalam menunjang program pariwisata halal dengan menggunakan pendekatan deskriptif kualitatif, dimana objek dan subjek penelitian melibatkan berbagai unsur, diantaranya: Dinas Pariwisata, Dinas Perindustrian dan Perdagangan, Dinas Lingkungan Hidup, Satpol PP dan MUI Kabupaten Siak. Hasil penelitian menunjukkan bahwa pengembangan destinasi wisata telah dioptimalkan sebaik mungkin, ditandai dengan promosi berkesinambungan, aksesibilitas yang semakin baik melalui jalinan kerjasama bersama Perkumpulan Pariwisata Halal Indonesia (PPHI) dan Siak Heritage Community (SHC), serta amenitas dan fasilitas pendukung yang diakomodir sejalan hadirnya konsumsi murah, halal dan baik bagi para wisatawan.
Sistem Pakar Deteksi Dini Gejala Covid-19 Menggunakan Metode Certainty Factor Berbasis Android Elvitaria, Luluk; Arisandi, Diki; Ardinan, Ardinan
Indonesian Journal of Intellectual Publication Vol. 2 No. 2 (2022): Maret 2022, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v2i2.325

Abstract

Wabah Covid-19 sudah ditetapkan oleh WHO sebagai pandemi global, tidak hanya di Indonesia. Hal ini menyebabkan kekhawatiran dan perubahan pola hidup, apalagi berdasarkan data dari WHO, lebih dari 500 juta jiwa telah terinfeksi Covid-19 diseluruh dunia. Untuk menekan jumlah penderita Covid-19 dan meningkatkan kesadaran bersama, perlu ada identifikasi secara mandiri agar gejala Covid-19 lebih mudah dikenali dan ditangani secara personal. Penelitian ini berfokus pada gejala yang umum terjadi berdasarkan informasi dari pakar (dokter dan tenaga kesehatan) yang berpengalaman menangani Covid-19. Informasi dari pakar kemudian diolah dengan menggunakan metode Certainty Factor (CF). Hasil dari penelitian ini adalah sebuah aplikasi berbasis mobile yang mampu mendeteksi gejala Covid-19 dengan menampilkan persentase jawaban berdasarkan pilihan dari pengguna. Setelah melalui pengujian dan evaluasi, metode CF menghasilkan akurasi sebesar 99.96 % untuk inferensi positif dan 99.760384 % untuk inferensi negatif. Selain dari sisi metode yang digunakan dalam penelitian ini, pengujian juga dilakukan dari sisi aplikasi. Hasil pengujian aplikasi dengan metode blackbox meyatakan bahwa setiap modul sukses mengidentifikasi gejala Covid -19.
Data-driven approach for batik pattern classification using convolutional neural network (CNN) Sari, Ira Puspita; Elvitaria, Luluk; Rudiansyah, Rudiansyah
Jurnal Mandiri IT Vol. 13 No. 3 (2025): January: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i3.361

Abstract

Batik is one of Indonesia's cultural heritages with complex and diverse patterns, possessing high artistic value and deep philosophy. Manual classification of batik patterns requires time and depends on expert knowledge, making the process inefficient. This study aims to develop a batik pattern classification model using Convolutional Neural Network (CNN) with a data-driven approach, enabling automatic and accurate pattern recognition. The dataset used consists of 4,284 batik images divided into five pattern classes: Kawung, Lereng, Ceplok, Parang, and Nitik. In this research, the CNN model was developed by using transfer learning techniques with MobileNetV3 pre-trained on a large dataset. The training process involved data augmentation to enhance the model's robustness against variations in batik patterns. The evaluation was conducted by measuring the model's accuracy and loss. The results show that the CNN model achieved an average accuracy of 93.42% on the training data and 93.88% on the testing data. This research demonstrates that the data-driven approach using CNN is effective for batik pattern classification, providing more accurate results compared to manual methods and offering an efficient solution for the digitalization of the batik industry. The developed model can serve as a foundation for broader applications in cultural preservation and the advancement of artificial intelligence-based technology.
An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification Elvitaria, Luluk; Ahmad, Ezak Fadzrin; Samsudin, Noor Azah; Ahmad Khalid, Shamsul Kamal; Salamun, -; Indra, Zul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2591

Abstract

Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architecture based on deep learning, which is named Okta-net. The architecture consists of 8 convolutional layers with separate convolution operations (SeparableConv2D). The output of the last convolution block will be fed to the fully connected layer using global average pooling. Meanwhile, in developing a deep learning model to classify batik image patterns, a dataset of 5 batik classes (motifs) was organized, consisting of 4,284 batik images. Through a series of experiments carried out, the proposed Okta-Net architecture succeeded in achieving satisfactory results with a validation accuracy of 93.17%, Precision of 91.60%, Recall of 92.28%, F-1 Score of 91.54%, and a loss of just 0.12%. Thus, it can be concluded that Okta-Net architecture can help preserve Indonesia's batik cultural heritage by accurate batik motif’s classification. Apart from that, based on a comparison of research outcomes, Okta-Net outperformed most of earlier studies, the majority of which had an accuracy of below 90%.
Classification of mushroom types based on digital image processing using convolutional neural network Sari, Ira Puspita; Elvitaria, Luluk
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.387

Abstract

In this research, a classification of mushroom types based on digital image processing using a Convolutional Neural Network (CNN) is conducted. The method employs the EfficientNet-B4 architecture as the base model utilizing transfer learning and fine-tuning processes. The dataset consists of 3000 types of mushrooms, each categorized into 10 classes with 300 images per class. The CNN model is implemented using the Python programming language on Google Colab editor. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score metrics to measure the model's performance. A comparison is made between all models with various training parameters, including identical and different settings. Additionally, the ratio of data splits, whether identical or different, is considered. Model 1, which utilizes a custom freeze layer and a data split ratio of 80% for training, 10% validation, and 10% testing, achieved the highest accuracy (90.00%), precision (90.09%), recall (89.63%), and F1-Score (89.59%) compared to other models. Therefore the implementation of a custom freeze layer to reduce the$ number of trainable parameters significantly impacts the accuracy level of the trained and tested model. Moreover, the determination of the data split ratio also slightly influences the accuracy level of the trained and tested model.
Co-Authors - Salamun Ahmad Khalid, Shamsul Kamal Ahmad, Ezak Fadzrin Amir Syamsuadi Anugerah, M Fajar Ardinan, Ardinan Arisandi, Diki Farras, Aqil Gianto Gianto Hartati, Seri Indra, Zul Ira Puspita Sari Liza Trisnawati MHD Rafi Yahya, MHD Rafi Noor Azah Samsudin, Noor Azah Nugroho, Sapto Setyo Puspa Ningrum Rudiansyah Rudiansyah, Rudiansyah Salamun Salamun Saputra, Haris Tri